### Abstract

Change-point detection is the problem of discovering time points at which properties of time-series data change. This covers a broad range of real-world problems and has been actively discussed in the community of statistics and data mining. In this paper, we present a novel non-parametric approach to detecting the change of probability distributions of sequence data. Our key idea is to estimate the ratio of probability densities, not the probability densities themselves. This formulation allows us to avoid non-parametric density estimation, which is known to be a difficult problem. We provide a change-point detection algorithm based on direct density-ratio estimation that can be computed very efficiently in an online manner. The usefulness of the proposed method is demonstrated through experiments using artificial and real datasets.

Original language | English |
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Title of host publication | Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics 133 |

Pages | 385-396 |

Number of pages | 12 |

Publication status | Published - Dec 1 2009 |

Externally published | Yes |

Event | 9th SIAM International Conference on Data Mining 2009, SDM 2009 - Sparks, NV, United States Duration: Apr 30 2009 → May 2 2009 |

### Publication series

Name | Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics |
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Volume | 1 |

### Other

Other | 9th SIAM International Conference on Data Mining 2009, SDM 2009 |
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Country | United States |

City | Sparks, NV |

Period | 4/30/09 → 5/2/09 |

### All Science Journal Classification (ASJC) codes

- Computational Theory and Mathematics
- Software
- Applied Mathematics

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## Cite this

*Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics 133*(pp. 385-396). (Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics; Vol. 1).